Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach

Among the smart capabilities promised by the next generation cellular networks (5G and beyond), it is fundamental that potential network anomalies are detected and timely treated to avoid critical issues concerning network performance, security, public safety. In this paper, we propose a comprehensi...

Descripción completa

Detalles Bibliográficos
Autores: Trinh, HD, Zeydan, E, Dini, P
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2019
País:España
Institución:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repositorio:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p1480
Acceso en línea:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1480
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078475814&doi=10.1109%2fACCESS.2019.2947742&partnerID=40&md5=5d9d18cc75a75da95aa709bbb2fd523d
Access Level:acceso abierto
Palabra clave:5G mobile communication systems
Anomaly detection
Data acquisition
Deep learning
Forecasting
Learning algorithms
Learning systems
Machine learning
Mobile telecommunication systems
Supervised learning
Traffic control
Wireless networks
Auto encoders
High resolution data
Next generation cellular networks
PDCCH
Physical downlink control channels (PDCCH)
Semi- supervised learning
State-of-the-art algorithms
Traffic prediction
Long short-term memory
id ES_12c88fc4e2cdc962279ec230210488d4
oai_identifier_str oai:cttc.fundanetsuite.com:p1480
network_acronym_str ES
network_name_str España
repository_id_str
spelling Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised ApproachTrinh, HDZeydan, EDini, P5G mobile communication systemsAnomaly detectionData acquisitionDeep learningForecastingLearning algorithmsLearning systemsMachine learningMobile telecommunication systemsSupervised learningTraffic controlWireless networksAuto encodersHigh resolution dataNext generation cellular networksPDCCHPhysical downlink control channels (PDCCH)Semi- supervised learningState-of-the-art algorithmsTraffic predictionLong short-term memoryAmong the smart capabilities promised by the next generation cellular networks (5G and beyond), it is fundamental that potential network anomalies are detected and timely treated to avoid critical issues concerning network performance, security, public safety. In this paper, we propose a comprehensive framework for detecting network anomalies using mobile traffic data: collecting data from the LTE Physical Downlink Control Channel (PDCCH) of different eNodeBs, we implement deep learning algorithms in a semi-supervised way to detect potential traffic anomalies that are generated, for example, by unexpected crowd gathering. With respect to other types of mobile dataset, using LTE PDCCH information, we are able to obtain fine-grained and high-resolution data for the users that are connected to the LTE eNodeB. Through a semi-supervised approach, algorithms are trained to detect anomalies using only one class of traffic samples. We design two algorithms based on stacked-LSTM Neural Networks: 1) LSTM Autoencoder (LSTM-AE), in which the objective is to reconstruct the traffic samples 2) LSTM traffic predictor (LSTM-PRED), where the goal is to predict the traffic in the next time-instants, based on historical data. In both cases, we analyze the reconstruction (or prediction) error to assess if the mobile traffic presents anomalies or not. Using the F1-score as metric, we demonstrate that the proposed methods are able to identify the anomalous traffic periods, beating a benchmark that comprises different state-of-The-Art algorithms for anomaly detection. © 2013 IEEE.Institute of Electrical and Electronics Engineers Inc.2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1480https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078475814&doi=10.1109%2fACCESS.2019.2947742&partnerID=40&md5=5d9d18cc75a75da95aa709bbb2fd523dIEEE AccessISSN: 21693536reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)Inglésinfo:eu-repo/semantics/openAccessoai:cttc.fundanetsuite.com:p14802026-06-17T11:44:47Z
dc.title.none.fl_str_mv Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach
title Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach
spellingShingle Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach
Trinh, HD
5G mobile communication systems
Anomaly detection
Data acquisition
Deep learning
Forecasting
Learning algorithms
Learning systems
Machine learning
Mobile telecommunication systems
Supervised learning
Traffic control
Wireless networks
Auto encoders
High resolution data
Next generation cellular networks
PDCCH
Physical downlink control channels (PDCCH)
Semi- supervised learning
State-of-the-art algorithms
Traffic prediction
Long short-term memory
title_short Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach
title_full Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach
title_fullStr Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach
title_full_unstemmed Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach
title_sort Detecting Mobile Traffic Anomalies through Physical Control Channel Fingerprinting: A Deep Semi-Supervised Approach
dc.creator.none.fl_str_mv Trinh, HD
Zeydan, E
Dini, P
author Trinh, HD
author_facet Trinh, HD
Zeydan, E
Dini, P
author_role author
author2 Zeydan, E
Dini, P
author2_role author
author
dc.subject.none.fl_str_mv 5G mobile communication systems
Anomaly detection
Data acquisition
Deep learning
Forecasting
Learning algorithms
Learning systems
Machine learning
Mobile telecommunication systems
Supervised learning
Traffic control
Wireless networks
Auto encoders
High resolution data
Next generation cellular networks
PDCCH
Physical downlink control channels (PDCCH)
Semi- supervised learning
State-of-the-art algorithms
Traffic prediction
Long short-term memory
topic 5G mobile communication systems
Anomaly detection
Data acquisition
Deep learning
Forecasting
Learning algorithms
Learning systems
Machine learning
Mobile telecommunication systems
Supervised learning
Traffic control
Wireless networks
Auto encoders
High resolution data
Next generation cellular networks
PDCCH
Physical downlink control channels (PDCCH)
Semi- supervised learning
State-of-the-art algorithms
Traffic prediction
Long short-term memory
description Among the smart capabilities promised by the next generation cellular networks (5G and beyond), it is fundamental that potential network anomalies are detected and timely treated to avoid critical issues concerning network performance, security, public safety. In this paper, we propose a comprehensive framework for detecting network anomalies using mobile traffic data: collecting data from the LTE Physical Downlink Control Channel (PDCCH) of different eNodeBs, we implement deep learning algorithms in a semi-supervised way to detect potential traffic anomalies that are generated, for example, by unexpected crowd gathering. With respect to other types of mobile dataset, using LTE PDCCH information, we are able to obtain fine-grained and high-resolution data for the users that are connected to the LTE eNodeB. Through a semi-supervised approach, algorithms are trained to detect anomalies using only one class of traffic samples. We design two algorithms based on stacked-LSTM Neural Networks: 1) LSTM Autoencoder (LSTM-AE), in which the objective is to reconstruct the traffic samples 2) LSTM traffic predictor (LSTM-PRED), where the goal is to predict the traffic in the next time-instants, based on historical data. In both cases, we analyze the reconstruction (or prediction) error to assess if the mobile traffic presents anomalies or not. Using the F1-score as metric, we demonstrate that the proposed methods are able to identify the anomalous traffic periods, beating a benchmark that comprises different state-of-The-Art algorithms for anomaly detection. © 2013 IEEE.
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1480
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078475814&doi=10.1109%2fACCESS.2019.2947742&partnerID=40&md5=5d9d18cc75a75da95aa709bbb2fd523d
url https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=1480
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078475814&doi=10.1109%2fACCESS.2019.2947742&partnerID=40&md5=5d9d18cc75a75da95aa709bbb2fd523d
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers Inc.
dc.source.none.fl_str_mv IEEE Access
ISSN: 21693536
reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname_str Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
reponame_str r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
collection r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403637285912577
score 15,812455

Similares en LA Referencia